Overview

Dataset statistics

Number of variables15
Number of observations99003
Missing cells177
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.3 MiB
Average record size in memory120.0 B

Variable types

Numeric14
Categorical1

Alerts

age is highly correlated with dob_yearHigh correlation
dob_year is highly correlated with ageHigh correlation
friend_count is highly correlated with friendships_initiatedHigh correlation
friendships_initiated is highly correlated with friend_countHigh correlation
likes is highly correlated with mobile_likes and 1 other fieldsHigh correlation
likes_received is highly correlated with mobile_likes_received and 1 other fieldsHigh correlation
mobile_likes is highly correlated with likesHigh correlation
mobile_likes_received is highly correlated with likes_received and 1 other fieldsHigh correlation
www_likes is highly correlated with likesHigh correlation
www_likes_received is highly correlated with likes_received and 1 other fieldsHigh correlation
likes_received is highly skewed (γ1 = 112.0745682) Skewed
mobile_likes_received is highly skewed (γ1 = 107.5312999) Skewed
www_likes_received is highly skewed (γ1 = 126.257317) Skewed
userid has unique values Unique
friend_count has 1962 (2.0%) zeros Zeros
friendships_initiated has 2997 (3.0%) zeros Zeros
likes has 22308 (22.5%) zeros Zeros
likes_received has 24428 (24.7%) zeros Zeros
mobile_likes has 35056 (35.4%) zeros Zeros
mobile_likes_received has 30003 (30.3%) zeros Zeros
www_likes has 60999 (61.6%) zeros Zeros
www_likes_received has 36864 (37.2%) zeros Zeros

Reproduction

Analysis started2022-10-30 10:58:24.342386
Analysis finished2022-10-30 10:59:06.553536
Duration42.21 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

userid
Real number (ℝ≥0)

UNIQUE

Distinct99003
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1597045.208
Minimum1000008
Maximum2193542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T16:29:06.687690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1000008
5-th percentile1060618.3
Q11298805.5
median1596148
Q31895744
95-th percentile2133357.1
Maximum2193542
Range1193534
Interquartile range (IQR)596938.5

Descriptive statistics

Standard deviation344059.1775
Coefficient of variation (CV)0.2154348391
Kurtosis-1.199556831
Mean1597045.208
Median Absolute Deviation (MAD)298438
Skewness0.0001076605667
Sum1.581122667 × 1011
Variance1.183767176 × 1011
MonotonicityNot monotonic
2022-10-30T16:29:06.836664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20943821
 
< 0.1%
10961601
 
< 0.1%
20957621
 
< 0.1%
19103221
 
< 0.1%
17876991
 
< 0.1%
11911011
 
< 0.1%
15693261
 
< 0.1%
10770051
 
< 0.1%
19354121
 
< 0.1%
18279111
 
< 0.1%
Other values (98993)98993
> 99.9%
ValueCountFrequency (%)
10000081
< 0.1%
10000131
< 0.1%
10000151
< 0.1%
10000381
< 0.1%
10000591
< 0.1%
10000611
< 0.1%
10000681
< 0.1%
10000941
< 0.1%
10001031
< 0.1%
10001251
< 0.1%
ValueCountFrequency (%)
21935421
< 0.1%
21935381
< 0.1%
21935221
< 0.1%
21934991
< 0.1%
21934851
< 0.1%
21934731
< 0.1%
21934681
< 0.1%
21934651
< 0.1%
21934601
< 0.1%
21934181
< 0.1%

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.28022383
Minimum13
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T16:29:06.985698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile15
Q120
median28
Q350
95-th percentile90
Maximum113
Range100
Interquartile range (IQR)30

Descriptive statistics

Standard deviation22.58974831
Coefficient of variation (CV)0.6059445462
Kurtosis1.561446767
Mean37.28022383
Median Absolute Deviation (MAD)10
Skewness1.415260654
Sum3690854
Variance510.2967289
MonotonicityNot monotonic
2022-10-30T16:29:07.380988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185196
 
5.2%
234404
 
4.4%
194391
 
4.4%
203769
 
3.8%
213671
 
3.7%
253641
 
3.7%
173283
 
3.3%
163086
 
3.1%
223032
 
3.1%
242827
 
2.9%
Other values (91)61703
62.3%
ValueCountFrequency (%)
13484
 
0.5%
141925
 
1.9%
152618
2.6%
163086
3.1%
173283
3.3%
185196
5.2%
194391
4.4%
203769
3.8%
213671
3.7%
223032
3.1%
ValueCountFrequency (%)
113202
 
0.2%
11218
 
< 0.1%
11118
 
< 0.1%
11015
 
< 0.1%
1099
 
< 0.1%
1081661
1.7%
10798
 
0.1%
106125
 
0.1%
10580
 
0.1%
10473
 
0.1%

dob_day
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.53040817
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T16:29:07.503082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median14
Q322
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.015606359
Coefficient of variation (CV)0.6204647697
Kurtosis-1.188960111
Mean14.53040817
Median Absolute Deviation (MAD)8
Skewness0.1078407568
Sum1438554
Variance81.28115802
MonotonicityNot monotonic
2022-10-30T16:29:07.617807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
17900
 
8.0%
104030
 
4.1%
153555
 
3.6%
53545
 
3.6%
123413
 
3.4%
23409
 
3.4%
33291
 
3.3%
173266
 
3.3%
203263
 
3.3%
143219
 
3.3%
Other values (21)60112
60.7%
ValueCountFrequency (%)
17900
8.0%
23409
3.4%
33291
3.3%
43217
3.2%
53545
3.6%
63108
 
3.1%
73010
 
3.0%
83202
3.2%
93003
 
3.0%
104030
4.1%
ValueCountFrequency (%)
311507
1.5%
302530
2.6%
292508
2.5%
282955
3.0%
272755
2.8%
262753
2.8%
253217
3.2%
242807
2.8%
232864
2.9%
222838
2.9%

dob_year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1975.719776
Minimum1900
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T16:29:07.755909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1923
Q11963
median1985
Q31993
95-th percentile1998
Maximum2000
Range100
Interquartile range (IQR)30

Descriptive statistics

Standard deviation22.58974831
Coefficient of variation (CV)0.01143368032
Kurtosis1.561446767
Mean1975.719776
Median Absolute Deviation (MAD)10
Skewness-1.415260654
Sum195602185
Variance510.2967289
MonotonicityNot monotonic
2022-10-30T16:29:07.888009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19955196
 
5.2%
19904404
 
4.4%
19944391
 
4.4%
19933769
 
3.8%
19923671
 
3.7%
19883641
 
3.7%
19963283
 
3.3%
19973086
 
3.1%
19913032
 
3.1%
19892827
 
2.9%
Other values (91)61703
62.3%
ValueCountFrequency (%)
1900202
 
0.2%
190118
 
< 0.1%
190218
 
< 0.1%
190315
 
< 0.1%
19049
 
< 0.1%
19051661
1.7%
190698
 
0.1%
1907125
 
0.1%
190880
 
0.1%
190973
 
0.1%
ValueCountFrequency (%)
2000484
 
0.5%
19991925
 
1.9%
19982618
2.6%
19973086
3.1%
19963283
3.3%
19955196
5.2%
19944391
4.4%
19933769
3.8%
19923671
3.7%
19913032
3.1%

dob_month
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.283365151
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T16:29:07.997086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.529671569
Coefficient of variation (CV)0.5617485987
Kurtosis-1.240397572
Mean6.283365151
Median Absolute Deviation (MAD)3
Skewness0.03129550742
Sum622072
Variance12.45858138
MonotonicityNot monotonic
2022-10-30T16:29:08.088099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
111772
11.9%
108476
8.6%
58271
8.4%
88266
8.3%
38110
8.2%
78021
8.1%
97939
8.0%
127894
8.0%
47810
7.9%
27632
7.7%
Other values (2)14812
15.0%
ValueCountFrequency (%)
111772
11.9%
27632
7.7%
38110
8.2%
47810
7.9%
58271
8.4%
67607
7.7%
78021
8.1%
88266
8.3%
97939
8.0%
108476
8.6%
ValueCountFrequency (%)
127894
8.0%
117205
7.3%
108476
8.6%
97939
8.0%
88266
8.3%
78021
8.1%
67607
7.7%
58271
8.4%
47810
7.9%
38110
8.2%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing175
Missing (%)0.2%
Memory size773.6 KiB
male
58574 
female
40254 

Length

Max length6
Median length4
Mean length4.814627434
Min length4

Characters and Unicode

Total characters475820
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowmale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male58574
59.2%
female40254
40.7%
(Missing)175
 
0.2%

Length

2022-10-30T16:29:08.200677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-30T16:29:08.328887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
male58574
59.3%
female40254
40.7%

Most occurring characters

ValueCountFrequency (%)
e139082
29.2%
m98828
20.8%
a98828
20.8%
l98828
20.8%
f40254
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter475820
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e139082
29.2%
m98828
20.8%
a98828
20.8%
l98828
20.8%
f40254
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Latin475820
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e139082
29.2%
m98828
20.8%
a98828
20.8%
l98828
20.8%
f40254
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII475820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e139082
29.2%
m98828
20.8%
a98828
20.8%
l98828
20.8%
f40254
 
8.5%

tenure
Real number (ℝ≥0)

Distinct2426
Distinct (%)2.5%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean537.8873749
Minimum0
Maximum3139
Zeros70
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T16:29:08.427893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47
Q1226
median412
Q3675
95-th percentile1575
Maximum3139
Range3139
Interquartile range (IQR)449

Descriptive statistics

Standard deviation457.6498739
Coefficient of variation (CV)0.8508284359
Kurtosis2.199058275
Mean537.8873749
Median Absolute Deviation (MAD)213
Skewness1.535680925
Sum53251388
Variance209443.4071
MonotonicityNot monotonic
2022-10-30T16:29:08.548666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300173
 
0.2%
303170
 
0.2%
242164
 
0.2%
272163
 
0.2%
257161
 
0.2%
297161
 
0.2%
280160
 
0.2%
285160
 
0.2%
284158
 
0.2%
278158
 
0.2%
Other values (2416)97373
98.4%
ValueCountFrequency (%)
070
0.1%
160
0.1%
272
0.1%
379
0.1%
486
0.1%
592
0.1%
693
0.1%
784
0.1%
887
0.1%
993
0.1%
ValueCountFrequency (%)
31393
< 0.1%
31291
 
< 0.1%
31281
 
< 0.1%
31011
 
< 0.1%
30191
 
< 0.1%
29581
 
< 0.1%
29261
 
< 0.1%
28881
 
< 0.1%
28221
 
< 0.1%
27881
 
< 0.1%

friend_count
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2562
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.3507873
Minimum0
Maximum4923
Zeros1962
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T16:29:08.679130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q131
median82
Q3206
95-th percentile720
Maximum4923
Range4923
Interquartile range (IQR)175

Descriptive statistics

Standard deviation387.304229
Coefficient of variation (CV)1.972511719
Kurtosis50.09427289
Mean196.3507873
Median Absolute Deviation (MAD)64
Skewness6.059008484
Sum19439317
Variance150004.5658
MonotonicityNot monotonic
2022-10-30T16:29:08.807312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01962
 
2.0%
11816
 
1.8%
21117
 
1.1%
3860
 
0.9%
5789
 
0.8%
4749
 
0.8%
10737
 
0.7%
24732
 
0.7%
6720
 
0.7%
29719
 
0.7%
Other values (2552)88802
89.7%
ValueCountFrequency (%)
01962
2.0%
11816
1.8%
21117
1.1%
3860
0.9%
4749
 
0.8%
5789
0.8%
6720
 
0.7%
7671
 
0.7%
8718
 
0.7%
9700
 
0.7%
ValueCountFrequency (%)
49231
< 0.1%
49171
< 0.1%
48631
< 0.1%
48451
< 0.1%
48441
< 0.1%
48261
< 0.1%
48171
< 0.1%
48031
< 0.1%
47971
< 0.1%
47941
< 0.1%

friendships_initiated
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1519
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.4524711
Minimum0
Maximum4144
Zeros2997
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T16:29:08.937057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q117
median46
Q3117
95-th percentile418
Maximum4144
Range4144
Interquartile range (IQR)100

Descriptive statistics

Standard deviation188.786951
Coefficient of variation (CV)1.756934475
Kurtosis42.53560096
Mean107.4524711
Median Absolute Deviation (MAD)36
Skewness5.150757415
Sum10638117
Variance35640.51287
MonotonicityNot monotonic
2022-10-30T16:29:09.069427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02997
 
3.0%
12212
 
2.2%
21551
 
1.6%
31355
 
1.4%
41352
 
1.4%
51328
 
1.3%
61328
 
1.3%
111319
 
1.3%
81314
 
1.3%
131279
 
1.3%
Other values (1509)82968
83.8%
ValueCountFrequency (%)
02997
3.0%
12212
2.2%
21551
1.6%
31355
1.4%
41352
1.4%
51328
1.3%
61328
1.3%
71237
1.2%
81314
1.3%
91245
1.3%
ValueCountFrequency (%)
41441
< 0.1%
36541
< 0.1%
35941
< 0.1%
35381
< 0.1%
34151
< 0.1%
32381
< 0.1%
32331
< 0.1%
30861
< 0.1%
30781
< 0.1%
30241
< 0.1%

likes
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2924
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.0787855
Minimum0
Maximum25111
Zeros22308
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T16:29:09.196443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median11
Q381
95-th percentile726
Maximum25111
Range25111
Interquartile range (IQR)80

Descriptive statistics

Standard deviation572.2806808
Coefficient of variation (CV)3.666614134
Kurtosis200.4456878
Mean156.0787855
Median Absolute Deviation (MAD)11
Skewness11.02370356
Sum15452268
Variance327505.1777
MonotonicityNot monotonic
2022-10-30T16:29:09.324332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
022308
22.5%
16928
 
7.0%
24434
 
4.5%
33240
 
3.3%
42507
 
2.5%
52027
 
2.0%
61806
 
1.8%
71618
 
1.6%
81430
 
1.4%
91381
 
1.4%
Other values (2914)51324
51.8%
ValueCountFrequency (%)
022308
22.5%
16928
 
7.0%
24434
 
4.5%
33240
 
3.3%
42507
 
2.5%
52027
 
2.0%
61806
 
1.8%
71618
 
1.6%
81430
 
1.4%
91381
 
1.4%
ValueCountFrequency (%)
251111
< 0.1%
216521
< 0.1%
167321
< 0.1%
165831
< 0.1%
147991
< 0.1%
143551
< 0.1%
140501
< 0.1%
140391
< 0.1%
136921
< 0.1%
136221
< 0.1%

likes_received
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct2681
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.6893629
Minimum0
Maximum261197
Zeros24428
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T16:29:09.474391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q359
95-th percentile561
Maximum261197
Range261197
Interquartile range (IQR)58

Descriptive statistics

Standard deviation1387.919613
Coefficient of variation (CV)9.726861091
Kurtosis17384.94
Mean142.6893629
Median Absolute Deviation (MAD)8
Skewness112.0745682
Sum14126675
Variance1926320.851
MonotonicityNot monotonic
2022-10-30T16:29:09.613905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024428
24.7%
17305
 
7.4%
24541
 
4.6%
33347
 
3.4%
42669
 
2.7%
52373
 
2.4%
61873
 
1.9%
71680
 
1.7%
81538
 
1.6%
91351
 
1.4%
Other values (2671)47898
48.4%
ValueCountFrequency (%)
024428
24.7%
17305
 
7.4%
24541
 
4.6%
33347
 
3.4%
42669
 
2.7%
52373
 
2.4%
61873
 
1.9%
71680
 
1.7%
81538
 
1.6%
91351
 
1.4%
ValueCountFrequency (%)
2611971
< 0.1%
1781661
< 0.1%
1520141
< 0.1%
1060251
< 0.1%
826231
< 0.1%
535341
< 0.1%
529641
< 0.1%
456331
< 0.1%
424491
< 0.1%
395361
< 0.1%

mobile_likes
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2396
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.1162995
Minimum0
Maximum25111
Zeros35056
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T16:29:09.745774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q346
95-th percentile481.9
Maximum25111
Range25111
Interquartile range (IQR)46

Descriptive statistics

Standard deviation445.2529851
Coefficient of variation (CV)4.195896268
Kurtosis360.9885806
Mean106.1162995
Median Absolute Deviation (MAD)4
Skewness14.16123656
Sum10505832
Variance198250.2207
MonotonicityNot monotonic
2022-10-30T16:29:09.874658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035056
35.4%
16297
 
6.4%
23941
 
4.0%
32917
 
2.9%
42265
 
2.3%
51794
 
1.8%
61598
 
1.6%
71395
 
1.4%
81212
 
1.2%
91149
 
1.2%
Other values (2386)41379
41.8%
ValueCountFrequency (%)
035056
35.4%
16297
 
6.4%
23941
 
4.0%
32917
 
2.9%
42265
 
2.3%
51794
 
1.8%
61598
 
1.6%
71395
 
1.4%
81212
 
1.2%
91149
 
1.2%
ValueCountFrequency (%)
251111
< 0.1%
216521
< 0.1%
167321
< 0.1%
140391
< 0.1%
135291
< 0.1%
129341
< 0.1%
126391
< 0.1%
121041
< 0.1%
120831
< 0.1%
119591
< 0.1%

mobile_likes_received
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct2004
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.1204913
Minimum0
Maximum138561
Zeros30003
Zeros (%)30.3%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T16:29:10.000752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q333
95-th percentile317
Maximum138561
Range138561
Interquartile range (IQR)33

Descriptive statistics

Standard deviation839.8894437
Coefficient of variation (CV)9.984362083
Kurtosis15522.64932
Mean84.1204913
Median Absolute Deviation (MAD)4
Skewness107.5312999
Sum8328181
Variance705414.2777
MonotonicityNot monotonic
2022-10-30T16:29:10.129781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
030003
30.3%
18243
 
8.3%
24948
 
5.0%
33608
 
3.6%
42944
 
3.0%
52383
 
2.4%
62022
 
2.0%
71745
 
1.8%
81521
 
1.5%
91437
 
1.5%
Other values (1994)40149
40.6%
ValueCountFrequency (%)
030003
30.3%
18243
 
8.3%
24948
 
5.0%
33608
 
3.6%
42944
 
3.0%
52383
 
2.4%
62022
 
2.0%
71745
 
1.8%
81521
 
1.5%
91437
 
1.5%
ValueCountFrequency (%)
1385611
< 0.1%
1312441
< 0.1%
899111
< 0.1%
733331
< 0.1%
434101
< 0.1%
307541
< 0.1%
303871
< 0.1%
273531
< 0.1%
207701
< 0.1%
189251
< 0.1%

www_likes
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1726
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.96242538
Minimum0
Maximum14865
Zeros60999
Zeros (%)61.6%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T16:29:10.263880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile208
Maximum14865
Range14865
Interquartile range (IQR)7

Descriptive statistics

Standard deviation285.5601519
Coefficient of variation (CV)5.715498191
Kurtosis449.1484832
Mean49.96242538
Median Absolute Deviation (MAD)0
Skewness16.91102529
Sum4946430
Variance81544.60033
MonotonicityNot monotonic
2022-10-30T16:29:10.386680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
060999
61.6%
14697
 
4.7%
22760
 
2.8%
31948
 
2.0%
41419
 
1.4%
51202
 
1.2%
61081
 
1.1%
7897
 
0.9%
8792
 
0.8%
9757
 
0.8%
Other values (1716)22451
 
22.7%
ValueCountFrequency (%)
060999
61.6%
14697
 
4.7%
22760
 
2.8%
31948
 
2.0%
41419
 
1.4%
51202
 
1.2%
61081
 
1.1%
7897
 
0.9%
8792
 
0.8%
9757
 
0.8%
ValueCountFrequency (%)
148651
< 0.1%
129031
< 0.1%
110771
< 0.1%
107631
< 0.1%
106271
< 0.1%
105391
< 0.1%
102551
< 0.1%
102321
< 0.1%
99021
< 0.1%
94311
< 0.1%

www_likes_received
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1636
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.56883125
Minimum0
Maximum129953
Zeros36864
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T16:29:10.514719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q320
95-th percentile227
Maximum129953
Range129953
Interquartile range (IQR)20

Descriptive statistics

Standard deviation601.416348
Coefficient of variation (CV)10.26853934
Kurtosis23812.2491
Mean58.56883125
Median Absolute Deviation (MAD)2
Skewness126.257317
Sum5798490
Variance361701.6237
MonotonicityNot monotonic
2022-10-30T16:29:10.646166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
036864
37.2%
18513
 
8.6%
25111
 
5.2%
33586
 
3.6%
42828
 
2.9%
52317
 
2.3%
61918
 
1.9%
71602
 
1.6%
81445
 
1.5%
91373
 
1.4%
Other values (1626)33446
33.8%
ValueCountFrequency (%)
036864
37.2%
18513
 
8.6%
25111
 
5.2%
33586
 
3.6%
42828
 
2.9%
52317
 
2.3%
61918
 
1.9%
71602
 
1.6%
81445
 
1.5%
91373
 
1.4%
ValueCountFrequency (%)
1299531
< 0.1%
621031
< 0.1%
396051
< 0.1%
392131
< 0.1%
340391
< 0.1%
326921
< 0.1%
293371
< 0.1%
231471
< 0.1%
226441
< 0.1%
150961
< 0.1%

Interactions

2022-10-30T16:29:03.569748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:38.731500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:40.652174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:42.620332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:44.516591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:46.378217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:48.428736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:50.371243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:52.235049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:54.181551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:55.965332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:57.789313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:59.800024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:01.703251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:03.701173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:38.876457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:40.778202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:42.764194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:44.646240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:46.510113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:48.562779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:50.500894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:52.525305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:54.316989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:56.097368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:57.920856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:59.937665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:01.837328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:03.820001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:38.999149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:40.902246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:42.891221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:44.769320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:46.636163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:48.696847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:50.626411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:52.662635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:54.437023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:56.222399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:58.044701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:00.087255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:01.963392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:03.945029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:39.141457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:41.029259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:43.019420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:44.901332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:46.920291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:48.839671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:50.798340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:52.784707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:54.564443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:56.348210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:58.179147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:00.219286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:02.092435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:04.073042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:39.273043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:41.168345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:43.169531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:45.032420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:47.095069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:48.992753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:50.928461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:52.908783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:54.690008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:56.473664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:58.310557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:00.347416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:02.237543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:04.206936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:39.409080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:41.284363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:43.298580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:45.164306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:47.240037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:49.126811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:51.058902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:53.034818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:54.821070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:56.598417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:58.437678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:00.473454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:02.371705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:04.340906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:39.546124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:41.407529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:43.426711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:45.301349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:47.373939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:49.264733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:51.186794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:53.161354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:54.947192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:56.735279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:58.570802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:00.613761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:02.505815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:04.468464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:39.689162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:41.534751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:43.559937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:45.428392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:47.502985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:49.402609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:51.310829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:53.286493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:55.073987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:56.866332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:58.697078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:00.753477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:02.632843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:04.596507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:39.822193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:41.671663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:43.690968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:45.558486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:47.636057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:49.546794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:51.439313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:53.404505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:55.196018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:56.991402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:58.815993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:00.902415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:02.766533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:04.730064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:39.954221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:41.825321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:43.823166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:45.696130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:47.770133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:49.683259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:51.576359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:53.526546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:55.313070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:57.118304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:59.143468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:01.029838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:02.895647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:04.865264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:40.090611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:41.980394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:43.965973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:45.832154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:47.897721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:49.820868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:51.706939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:53.659575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:55.442127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:57.251317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:59.276638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:01.162556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:03.026981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:04.993349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:40.228673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:42.109789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:44.100090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:45.956158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:48.029317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:49.955019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:51.827967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:53.785649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:55.567397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:57.379410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:59.399729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:01.294420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:03.156078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:05.129277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:40.374870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:42.358894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:44.248710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:46.095121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:48.170568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:50.099022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:51.962985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:53.917740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:55.698182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:57.515452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:59.533483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:01.431239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:03.299169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:05.271361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:40.511110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:42.492466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:44.381065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:46.242137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:48.298156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:50.236639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:52.102711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:54.053046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:55.840287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:57.651212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:28:59.667998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:01.566442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-30T16:29:03.436797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-30T16:29:10.794288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-10-30T16:29:10.988143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-30T16:29:11.231568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-30T16:29:11.429796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-30T16:29:11.629892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-30T16:29:05.490721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-30T16:29:05.866516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-30T16:29:06.231367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-30T16:29:06.363444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

useridagedob_daydob_yeardob_monthgendertenurefriend_countfriendships_initiatedlikeslikes_receivedmobile_likesmobile_likes_receivedwww_likeswww_likes_received
020943821419199911male266.000000000
11192601142199911female6.000000000
220838841416199911male13.000000000
312031681425199912female93.000000000
41733186144199912male82.000000000
51524765141199912male15.000000000
61136133131420001male12.000000000
7168036113420001female0.000000000
8136517413120001male81.000000000
9171256713220002male171.000000000

Last rows

useridagedob_daydob_yeardob_monthgendertenurefriend_countfriendships_initiatedlikeslikes_receivedmobile_likesmobile_likes_receivedwww_likeswww_likes_received
989931654565191519948male394.04538414445011508844355961669127
98994206300620419931female402.01988332735110602572487333310332692
989951132164209199310female699.03611973450777684414690993859
989961668695242519894female182.0293812726018177655843117081756057
9899714589852814198512female290.022181618462610268429042503366018
98998126829968419454female541.021183413996180893505118874916202
989991256153181219953female21.01968172044011341243991059222820
990001195943151019985female111.0200215241195912554119591146201092
990011468023231119904female416.0256018545066516450657600756
990021397896391519745female397.020497689410124439410953002913